Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea

With global climate changing, the carbon dioxide (CO2) absorption rates increased in marginal seas. Due to the limited availability of in-situ spatial and temporal distribution data, the current status of the sea surface carbon dioxide partial pressure (pCO(2)) in the Yellow Sea is unclear. Therefore, a pCO(2) model based on a random forest algorithm has been developed, which was trained and tested using 14 cruise data sets from 2011 to 2019, and remote sensing satellite sea surface temperature, chlorophyll concentration, diffuse attenuation of downwelling irradiance, and in-situ salinity were used as the input variables. The seasonal and interannual variations of modeled pCO(2) were discussed from January 2003 and December 2021 in the Yellow Sea. The results showed that the model developed for this study performed well, with a root mean square difference (RMSD) of 43 mu atm and a coefficient of determination (R-2) of 0.67. Moreover, modeled pCO(2) increased at a rate of 0.36 mu atm year(-1) (R-2 = 0.27, p < 0.05) in the YS, which is much slower than the rate of atmospheric pCO(2) (pCO(2)(air)) rise. The reason behind it needs further investigation. Compared with pCO(2) from other datasets, the pCO(2) derived from the RF model exhibited greater consistency with the in-situ pCO(2) (RMSD = 55 mu atm). In general, the RF model has significant improvement over the previous models and the global data sets.

Automated summary

With global climate changing, the absorption of carbon dioxide (CO2) in marginal seas has increased. The current status of sea surface carbon dioxide partial pressure (pCO(2)) in the Yellow Sea is unclear due to limited availability of in-situ data. Therefore, a pCO(2) model based on a random forest algorithm was developed using cruise data from 2011 to 2019 and remote sensing satellite data. The model showed good performance and revealed seasonal and interannual variations of pCO(2) in the Yellow Sea.